SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 851875 of 10419 papers

TitleStatusHype
Evidential Turing ProcessesCode1
EViT: An Eagle Vision Transformer with Bi-Fovea Self-AttentionCode1
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
Evolving Attention with Residual ConvolutionsCode1
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuningCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use caseCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
Explaining Latent Representations with a Corpus of ExamplesCode1
A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-ScansCode1
Discovering and Mitigating Visual Biases through Keyword ExplanationCode1
ClusterFormer: Clustering As A Universal Visual LearnerCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Eye-gaze Guided Multi-modal Alignment for Medical Representation LearningCode1
A Novel Convolutional Neural Network Architecture with a Continuous SymmetryCode1
A Comprehensive Empirical Evaluation on Online Continual LearningCode1
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking TestbedCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep LearningCode1
Fast and Private Inference of Deep Neural Networks by Co-designing Activation FunctionsCode1
Fast AutoAugmentCode1
CLR: Channel-wise Lightweight Reprogramming for Continual LearningCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Clusterability as an Alternative to Anchor Points When Learning with Noisy LabelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified